Scientific communication relies on language that conveys different levels of certainty about research findings. In observational studies, causal language-which attributes cause-and-effect relationships-is an important way researchers express certainty, as they must balance confidence in their findings against the limitations of observational data. Ideally, the use of causal language should depend only on the strength of the underlying evidence. However, through the analysis of over 90,000 abstracts from observational studies using computational linguistic and regression methods, we found that causal language is more common in work by less experienced authors, smaller research teams, male last authors, and researchers from countries with higher uncertainty avoidance indices-a cultural dimension reflecting a society's preference for certainty over ambiguity. Our findings suggest that the use of causal language is not solely driven by the strength of evidence, but also by the sociocultural backgrounds of authors and their team composition. This work provides a new perspective for understanding systematic patterns in how scientists express certainty, emphasizing the importance of recognizing these human factors when evaluating scientific claims.

The use of causal language in observational studies has raised concerns about overstatement in scientific communication. While some argue that such language should be reserved for randomized controlled trials, others contend that rigorous causal inference methods can justify causal claims in observational research. Ideally, causal language should align with the strength of the underlying evidence. However, through the analysis of over 90,000 abstracts from observational studies using computational linguistic and regression methods, we found that causal language are more common in work by less experienced authors, smaller research teams, male last authors, and researchers from countries with higher uncertainty avoidance indices. Our findings suggest that the use of causal language is not solely driven by the strength of evidence, but also by the sociocultural backgrounds of authors and their team composition. This work provides a new perspective for understanding systematic variations in scientific communication and emphasizes the importance of recognizing these human factors when evaluating scientific claims.

Accurately linking news articles to scientific research works is a critical component in a number of applications, such as measuring the social impact of a research work and detecting inaccuracies or distortions in science news. Although the lack of links between news and literature has been a challenge in these applications, it is a relatively unexplored research problem. In this paper we designed and evaluated a new approach that consists of (1) augmenting latest named-entity recognition techniques to extract various metadata, and (2) designing a new elastic search engine that can facilitate the use of enriched metadata queries. To evaluate our approach, we constructed two datasets of paired news articles and research papers: one is used for training models to extract metadata, and the other for evaluation. Our experiments showed that the new approach performed significantly better than a baseline approach used by altmetric.com (0.89 vs 0.32 in terms of top-1 accuracy). To further demonstrate the effectiveness of the approach, we also conducted a study on 37,600 health-related press releases published on EurekAlert!, which showed that our approach was able to identify the corresponding research papers with a top-1 accuracy of at least 0.97.

This demo system presents a browser extension that allows the reader of a health news article to quickly retrieve related medical/health research papers. This system can help news editors and readers fact-check health news for incorrect or exaggerated claims, such as making causal claims from correlational findings or inference of animal studies to humans. Linking health news to the original research papers is not a trivial task, as links are largely missing in science news reports. To link health news to medical literature, our system includes a new named-entity recognition function to extract journal names, and a new Elasticsearch-based search engine to incorporate rich metadata into the search strategy. This paper also introduces a new dataset for evaluating the performance of the proposed search system.

Prior studies have found that women self-promote less than men due to gender stereotypes. In this study we built a BERT-based NLP model to predict whether a Congressional tweet shows self-promotion or not and then used this model to examine whether a gender gap in self-promotion exists among Congressional tweets. After analyzing 2 million Congressional tweets from July 2017 to March 2021, controlling for a number of factors that include political party, chamber, age, number of terms in Congress, number of daily tweets, and number of followers, we found that women in Congress actually perform more self-promotion on Twitter, indicating a reversal of traditional gender norms where women self-promote less than men.

Press releases have an increasingly strong influence on media coverage of health research; however, they have been found to contain seriously exaggerated claims that can misinform the public and undermine public trust in science. In this study we propose an NLP approach to identify exaggerated causal claims made in health press releases that report on observational studies, which are designed to establish correlational findings, but are often exaggerated as causal. We developed a new corpus and trained models that can identify causal claims in the main statements in a press release. By comparing the claims made in a press release with the corresponding claims in the original research paper, we found that 22% of press releases made exaggerated causal claims from correlational findings in observational studies. Furthermore, universities exaggerated more often than journal publishers by a ratio of 1.5 to 1. Encouragingly, the exaggeration rate has slightly decreased over the past 10 years, despite the increase of the total number of press releases. More research is needed to understand the cause of the decreasing pattern.

Causal interpretation of correlational findings from observational studies has been a major type of misinformation in science communication. Prior studies on identifying inappropriate use of causal language relied on manual content analysis, which is not scalable for examining a large volume of science publications. In this study, we first annotated a corpus of over 3,000 PubMed research conclusion sentences, then developed a BERT-based prediction model that classifies conclusion sentences into "no relationship", "correlational", "conditional causal", and "direct causal" categories, achieving an accuracy of 0.90 and a macroF1 of 0.88. We then applied the prediction model to measure the causal language use in the research conclusions of about 38,000 observational studies in PubMed. The prediction result shows that 21.7% studies used direct causal language exclusively in their conclusions, and 32.4% used some direct causal language. We also found that the ratio of causal language use differs among authors from different countries, challenging the notion of a shared consensus on causal language use in the global science community. Our prediction model could also be used to help identify the inappropriate use of causal language in science publications.

Two key problems for crowd-sourcing systems are motivating contributions from participants and ensuring the quality of these contributions. Games have been suggested as a motivational approach to encourage contribution, but attracting participation through game play rather than intrinsic interest raises concerns about the quality of the contributions provided. These concerns are particularly important in the context of citizen science projects, when the contributions are data to be used for scientific research. To assess the validity of concerns about the effects of gaming on data quality, we compare the quality of data obtained from two citizen science games, one a "gamified" version of a species classification task and one a fantasy game that used the classification task only as a way to advance in the game play. Surprisingly, though we did observe cheating in the fantasy game, data quality (i.e., classification accuracy) from participants in the two games was not significantly different. As well, data from short-time contributors was also at a usable level of accuracy. Finally, learning did not seem to affect data quality in our context. These findings suggest that various approaches to gamification can be useful for motivating contributions to citizen science projects.

As multicore computer systems become increasingly complex, parallel simulation is becoming an important tool for exploring design space and evaluating design tradeoffs. The key to the success of parallel simulation is the ability to maintain a high degree of parallelism under synchronization constraints. In this article, an enhanced Null-message algorithm called FNM is presented that uses domain-specific knowledge to improve the performance of the basic Null-message algorithm. Based on their runtime states, the components of the simulation model can make a conservative forecast of future interprocess events. The forecast information is carried in the enhanced Null-messages, and, by combining the forecast from both sides of an interprocess link, FNM can achieve a dynamic system lookahead that is much greater than what the static system structure provides. This improved lookahead allows better exploitation of the simulation model's inherent parallelism and leads to better performance. Compared with the basic Null-message algorithm, FNM greatly reduces the amount of Null-messages and improves parallel simulation performance as a result, while at the same time it guarantees simulation correctness as the basic Null-message algorithm does. In tests on cycle-level models with up to 128 cores, FNM shows good scalability and proves to be an effective method.

This paper proposes a novel obstacle detection and feedback system iSee based on RGB-D sensor for the blind. Firstly, a fast and robust ground detection method based on multi-scale voxel plane segmentation and geometric constraints is introduced. Secondly, the regions besides the ground area are segmented by region growing. Then an area-division based obstacle detection method is proposed, which combines the analysis of context information. Finally, a multi-level voice feedback strategy is proposed. The experimental results show the proposed system can detect and feedback obstacle information fast and robustly.

A key problem for crowd-sourcing systems is motivating contributions from participants and ensuring the quality of these contributions. Games have been suggested as a motivational approach to encourage contribution, but attracting participation through game play rather than scientific interest raises concerns about the quality of the data provided, which is particularly important when the data are to be used for scientific research. To assess whether these concerns are justified, we compare the quality of data obtained from two citizen science games, one a “gamified” version of a species classification task and one a fantasy game that used the classification task only as a way to advance in the game play. Surprisingly, though we did observe cheating in the fantasy game, data quality (i.e., classification accuracy) from participants in the two games was not significantly different. As well, the quality of data from short-time contributors was at a usable level of accuracy. These findings suggest that various approaches to gamification can be useful for motivating contributions to citizen science projects.

Manifold is a parallel simulation framework for multi-core systems. For full-system simulation, Manifold adopts the timing-directed simulation paradigm that separates the simulation into a functional front-end and a timing back-end. Components in the front-end perform functional simulation of the cores and send streams of instructions to the back-end to simulate the timing behavior. In its current design, Manifold uses the QSim multi-core emulator as the front-end, which communicates with the back-end through network sockets. Experiments have shown that the latency of the socket communications has a significant impact on the overall simulation performance. This paper presents a novel method that attempts to hide the TCP/IP latency for the back-end by creating proxy processes as an intermediary between the front-end and the back-end. The proxies serve as clients to the QSim server in the front-end, and as servers to the back-end. They interact with the QSim server through sockets, while working with the back-end in a producer-consumer manner using shared memory segments. Experiments show that this method can completely hide the TCP/IP latency for the back-end. The back-end can always get its instructions from the shared memory without waiting for the QSim server. The overhead of getting inputs for the back-end simulation is reduced to almost zero. As confirmed by our experiments, this improvement causes some side effects that together lead to significant improvements in overall simulation performance. In testing of system models with up to 64 cores, we have achieved from 29% to 51% improvement in simulation performance.

This paper presents Manifold, an open-source parallel simulation framework for multicore architectures. It consists of a parallel simulation kernel, a set of microarchitecture components, and an integrated library of power, thermal, reliability, and energy models. Using the components as building blocks, users can assemble multicore architecture simulation models and perform serial or parallel simulations to study the architectural and/or the physical characteristics of the models. Users can also create new components for Manifold or port existing models. Importantly, Manifold's component-based design provides the user with the ability to easily replace a component with another for efficient explorations of the design space. It also allows components to evolve independently and making it easy for simulators to incorporate new components as they become available. The distinguishing features of Manifold include i) transparent parallel execution, ii) integration of power, thermal, reliability, and energy models, iii) full system simulation, e.g., operating system and system binaries, and iv) component-based design. In this paper we provide a description of the software architecture of Manifold, and its main elements - a parallel multicore emulator front-end and a parallel component-based back-end timing model. We describe a few simulators that are built with Manifold components to illustrate its flexibility, and present test results of the scalability obtained on full-system simulation of coherent shared-memory multicore models with 16, 32, and 64 cores executing PARSEC and SPLASH-2 benchmarks.

There has been little research that studies the effect of partitioning on parallel simulation of multicore systems. This paper presents our study of this important problem in the context of Null-message-based synchronization algorithm for parallel multicore simulation. This paper focuses on coarse grain parallel simulation where each core and its cache slices are modeled within a single logical process (LP) and different partitioning schemes are only applied to the interconnection network. In this paper we show that encapsulating the entire on-chip interconnection network into a single logical process is an impediment to scalable simulation. This baseline partitioning and two other schemes are investigated. Experiments are conducted on a subset of the PARSEC benchmarks with 16-, 32-, 64- and 128-core models. Results show that the partitioning scheme has a significant impact on simulation performance and parallel efficiency. Beyond a certain system scale, one scheme consistently outperforms the other two schemes, and the performance as well as efficiency gaps increases as the size of the model increases - with up to 4.1 times faster speed and 277% better efficiency for 128-core models. We explain the reasons for this behavior, which can be traced to the features of the Null-message-based synchronization algorithm. Because of this, we believe that, if a component has increasing number of inter-LP interactions with increasing system size, such components should be partitioned into several sub-components to achieve better performance.

This paper presents two optimization techniques for the basic Null-message algorithm in the context of parallel simulation of multicore computer architectures. Unlike the general, application-independent optimization methods, these are application-specific optimizations that make use of system properties of the simulation application. We demonstrate in two aspects that the domain-specific knowledge offers great potential for optimization. First, it allows us to send Null-messages much less eagerly, thus greatly reducing the amount of Null-messages. Second, the internal state of the simulation application allows us to make conservative forecast of future outgoing events. This leads to the creation of an enhanced synchronization algorithm called Forecast Null-message algorithm, which, by combining the forecast from both sides of a link, can greatly improve the simulation look-ahead. Compared with the basic Null-message algorithm, our optimizations greatly reduce the number of Null-messages and increase simulation performance significantly as a result. On a subset of the PARSEC benchmarks, a maximum speedup of about 6 is achieved with 17 LPs.

Background: Consumer and patient participation proved to be an effective approach for medical pictogram design, but it can be costly and time-consuming. We proposed and evaluated an inexpensive approach that crowdsourced the pictogram evaluation task to Amazon Mechanical Turk (MTurk) workers, who are usually referred to as the "turkers".Objective: To answer two research questions: (1) Is the turkers' collective effort effective for identifying design problems in medical pictograms? and (2) Do the turkers' demographic characteristics affect their performance in medical pictogram comprehension?Methods: We designed a Web-based survey (open-ended tests) to ask 100 US turkers to type in their guesses of the meaning of 20 US pharmacopeial pictograms. Two judges independently coded the turkers' guesses into four categories: correct, partially correct, wrong, and completely wrong. The comprehensibility of a pictogram was measured by the percentage of correct guesses, with each partially correct guess counted as 0.5 correct. We then conducted a content analysis on the turkers' interpretations to identify misunderstandings and assess whether the misunderstandings were common. We also conducted a statistical analysis to examine the relationship between turkers' demographic characteristics and their pictogram comprehension performance.Results: The survey was completed within 3 days of our posting the task to the MTurk, and the collected data are publicly available in the multimedia appendix for download. The comprehensibility for the 20 tested pictograms ranged from 45% to 98%, with an average of 72.5%. The comprehensibility scores of 10 pictograms were strongly correlated to the scores of the same pictograms reported in another study that used oral response-based open-ended testing with local people. The turkers' misinterpretations shared common errors that exposed design problems in the pictograms. Participant performance was positively correlated with their educational level.Conclusions: The results confirmed that crowdsourcing can be used as an effective and inexpensive approach for participatory evaluation of medical pictograms. Through Web-based open-ended testing, the crowd can effectively identify problems in pictogram designs. The results also confirmed that education has a significant effect on the comprehension of medical pictograms. Since low-literate people are underrepresented in the turker population, further investigation is needed to examine to what extent turkers' misunderstandings overlap with those elicited from low-literate people.

This poster proposes a human computation approach to collecting representative pictures for words so that the collected pictures can efficiently and effectively convey the meaning of the words or concepts. A large collection of representative pictures can be used in text-to-picture communication systems, and may also be used to teach computers to learn what representative pictures are. We have developed a web application to help players of Draw Something, a popular social mobile game, search pictures for drawing inspiration while at the same time they implicitly help us collect representative pictures for words. Our preliminary result shows that the proposed approach has the potential to harvest Draw Something players for collecting desired data.

A simulation system for modern multicore architectures is composed of various component models. For such a system to be useful for research purposes, modifiability is a key quality attribute. Users, when building a simulation model, need to have the capability to adjust various aspects of a component, or even replace a component with another of the same type. Software design considerations can determine whether or not a simulation system is successful in providing such capabilities. This paper presents a few design tactics that we adopt in creating configurable, modifiable, and reusable components for Manifold, our parallel simulation framework for multicore systems. The main example component is MCP-cache, a coherence cache model. The ideas behind the tactics are general enough and should be useful to designers of similar systems.